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Artificial Neural Network as a Tool for Estimation of the Higher Heating Value of Miscanthus Based on Ultimate Analysis

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  • Ivan Brandić

    (Faculty of Agriculture, University of Zagreb, Svetošimunska Cesta 25, 10000 Zagreb, Croatia)

  • Lato Pezo

    (Institute of General and Physical Chemistry, University of Belgrade, Studentski trg 12/V, 11000 Belgrade, Serbia)

  • Nikola Bilandžija

    (Faculty of Agriculture, University of Zagreb, Svetošimunska Cesta 25, 10000 Zagreb, Croatia)

  • Anamarija Peter

    (Faculty of Agriculture, University of Zagreb, Svetošimunska Cesta 25, 10000 Zagreb, Croatia)

  • Jona Šurić

    (Faculty of Agriculture, University of Zagreb, Svetošimunska Cesta 25, 10000 Zagreb, Croatia)

  • Neven Voća

    (Faculty of Agriculture, University of Zagreb, Svetošimunska Cesta 25, 10000 Zagreb, Croatia)

Abstract

Miscanthus is a perennial energy crop that produces high yields and has the potential to be converted into energy. The ultimate analysis determines the composition of the biomass and the energy value in terms of the higher heating value (HHV), which is the most important parameter in determining the quality of the fuel. In this study, an artificial neural network (ANN) model based on the principle of supervised learning was developed to predict the HHV of miscanthus biomass. The developed ANN model was compared with the models of predictive regression models (suggested from the literature) and the accuracy of the developed model was determined by the coefficient of determination. The paper presents data from 192 miscanthus biomass samples based on ultimate analysis and HHV. The developed model showed good properties and the possibility of prediction with high accuracy (R 2 = 0.77). The paper proves the possibility of using ANN models in practical application in determining fuel properties of biomass energy crops and greater accuracy in predicting HHV than the regression models offered in the literature.

Suggested Citation

  • Ivan Brandić & Lato Pezo & Nikola Bilandžija & Anamarija Peter & Jona Šurić & Neven Voća, 2022. "Artificial Neural Network as a Tool for Estimation of the Higher Heating Value of Miscanthus Based on Ultimate Analysis," Mathematics, MDPI, vol. 10(20), pages 1-12, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:20:p:3732-:d:938959
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    References listed on IDEAS

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    1. Noushabadi, Abolfazl Sajadi & Dashti, Amir & Ahmadijokani, Farhad & Hu, Jinguang & Mohammadi, Amir H., 2021. "Estimation of higher heating values (HHVs) of biomass fuels based on ultimate analysis using machine learning techniques and improved equation," Renewable Energy, Elsevier, vol. 179(C), pages 550-562.
    2. Kartal, Furkan & Özveren, Uğur, 2020. "A deep learning approach for prediction of syngas lower heating value from CFB gasifier in Aspen plus®," Energy, Elsevier, vol. 209(C).
    3. Ioannis O. Vardiambasis & Theodoros N. Kapetanakis & Christos D. Nikolopoulos & Trinh Kieu Trang & Toshiki Tsubota & Ramazan Keyikoglu & Alireza Khataee & Dimitrios Kalderis, 2020. "Hydrochars as Emerging Biofuels: Recent Advances and Application of Artificial Neural Networks for the Prediction of Heating Values," Energies, MDPI, vol. 13(17), pages 1-20, September.
    4. Koçar, Günnur & Civaş, Nilgün, 2013. "An overview of biofuels from energy crops: Current status and future prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 900-916.
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    Cited by:

    1. Justyna Kujawska & Monika Kulisz & Piotr Oleszczuk & Wojciech Cel, 2023. "Improved Prediction of the Higher Heating Value of Biomass Using an Artificial Neural Network Model Based on the Selection of Input Parameters," Energies, MDPI, vol. 16(10), pages 1-16, May.
    2. Ivan Brandić & Lato Pezo & Nikola Bilandžija & Anamarija Peter & Jona Šurić & Neven Voća, 2023. "Comparison of Different Machine Learning Models for Modelling the Higher Heating Value of Biomass," Mathematics, MDPI, vol. 11(9), pages 1-14, April.
    3. Mislav Kontek & Luka Brezinščak & Vanja Jurišić & Ivan Brandić & Alan Antonović & Božidar Matin & Karlo Špelić & Tajana Krička & Ana Matin, 2023. "Mitigating the Energy Crisis: Utilization of Seed Production Wastes for Energy Production in Continental Croatia," Energies, MDPI, vol. 16(2), pages 1-11, January.

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